class_docstr: Greedy sampler always choosing the class with the highest probability.
docstr: Selects the cluster IDs with with the highest probability.
  
  Arguments:
    logits: A float tensor of shape `[batch_size, num_clusters]` representing
      a batch of log likelihoods for all possible clusters.
  
  Returns:
    An int tensor of shape `[batch_size]`, the sampled cluster IDs.
docstr_tok: ["Selects","the","cluster","IDs","with","with","the","highest","probability",".","Arguments",":","logits",":","A","float","tensor","of","shape","`","[","batch_size",",","num_clusters","]","`","representing","a","batch","of","log","likelihoods","for","all","possible","clusters",".","Returns",":","An","int","tensor","of","shape","`","[","batch_size","]","`",",","the","sampled","cluster","IDs","."]
code: def sample(self, logits):
      """Selects the cluster IDs with with the highest probability.
  
      Arguments:
        logits: A float tensor of shape `[batch_size, num_clusters]` representing
          a batch of log likelihoods for all possible clusters.
  
      Returns:
        An int tensor of shape `[batch_size]`, the sampled cluster IDs.
      """
      return tf.argmax(logits, axis=-1, output_type=tf.int32)
  
file_path: neural-structured-learning/research/neural_clustering/models/ncp_wrapper.py
imports: from neural_clustering.utils.data_utils import batch_remap_label_ids
  import tensorflow
label_desc: Use image node_id 1 for calling the GreedySampler obj's underlying member method code with example usage: obj.sample(logits) and returns: tf